NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Fuzzy

NCSR “DEMOKRITOS”Institute of Nuclear Technologyand Radiation Protection

NATIONAL TECHNICAL UNIVERSITY OF ATHENS

School of Chemical Engineering

Fuzzy Systems in Use for Human Reliability Analysis

Myrto Konstandinidou

Zoe Nivolianitou

Nikolaos Markatos

Christos Kyranoudis

Loss Prevention Prague, June 2004




Outline

Introduction The Fuzzy Logic as a modeling tool Methods for Human Reliability Analysis The CREAM methodology Development of the Fuzzy Classification System Results Conclusions




Introduction

HRA is a critical element for PRA

Most important concerns:

- the subjectivity of the methods

- the uncertainty of data

- the complexity of the human factor per se

Fuzzy logic theory has had many relevant

applications in the last years




Fuzzy Logic as a modeling tool (1)

Fuzzy logic (FL) is a very useful tool for modeling- complex systems

- qualitative, inexact or uncertain information

• FL resembles the way humans make inference and take decisions

FL accommodates ambiguities of real world human language and logic





Applications

- Automatic control

- Data classification

- Decision analysis

- Computer Vision

- Expert systems

The most used fuzzy inference method:

Mamdani’s method(1975)





DefinitionsFL allows an object to be a member of more that

one sets and to partially belong to them.

- Fuzzy set

- Degree of membership

- Partial membership





The 3 steps of a FL system

Fuzzification: the process of decomposing input variables to fuzzy sets

Fuzzy Inference: a method to interpret the values of the input vectors

Defuzzification: the process of weighting and averaging the outputs

Crisp OutputCrisp Input

Fuzzification

Inference

Defuzzification




Methods of Human Reliability Analysis

Fundamental Limitations– Insufficient data– Methodological limitations– Uncertainty

Most important methods developed for HRA:– THERP– CREAM– ATHEANA




CREAM Methodology (1)

The choice of CREAM was made because:

1) It is well structured and precise

2) It fits better in the general structure of FL

3) It presents a consistent error classification system

4) This system integrates individual, technological and

organizational factors




CREAM Methodology (2)

Control Modes

1. Scrambled

2. Opportunistic

3. Tactical

4. Strategic

Definition of Common Performance Conditions (CPCs) to be used in FL model




Development of a Fuzzy Classifier (1)

Experience

- Accident analysis

- Risk assessment

- Human reliabilityData

- Diagrams of CREAM

- MARS Database

- Incidents and accidents from

the Greek Petrochemical Industry





The Development of the Fuzzy Classification System for Human Reliability Analysis

STEP 1Selection of input

parameters

STEP 2Development of

the Fuzzy sets

STEP 3Development of the Fuzzy Rules





STEP 1: Selection of the input parameters

Adequacy of organization

Number of simultaneous

goals

Crew collaboration

quality

Working conditions

Available time Adequacy of training

Adequacy of maintenance &

support

Availability of procedures &

plans

Time of day (Circadian

rhythm)





STEP 2: Development of the Fuzzy sets

Each input is given a number based on its quality

0 (worst case) - 100 (best case)

“Time of day” from 0:00 (midnight) to 24:00

Output scale 0.5*10-5 - 1.0*100





CPCs Fuzzy sets

INPUT Adequacy of organization 4

Working conditions 3

Availability of procedures 3

Adequacy of maintenance 4

No of simultaneous goals 3

Available time 3

Time of day 3

Adequacy of training 3

Crew collaboration quality 4

OUTPUT Probability of human erroneous action 4





Output fuzzy sets:Probability of a human erroneous action

Control mode Action failure probability

Strategic 0.5*10-5<p<1.0*10-2

Tactical 1.0*10-3<p<1.0*10-1

Opportunistic 1.0*10-2<p<0.5*100

Scrambled 1.0*10-1<p<1.0*100




Quality of Working Conditions

0

1

0 10 20 30 40 50 60 70 80 90 100

Working conditions Incompatible

Compatible

Advantageous


Input variable





Action Failure Probability

0

1

-5.30E+00 -4.30E+00 -3.30E+00 -2.30E+00 -1.30E+00 -3.00E-01

Probability interval StrategicTacticalOpportunisticScrambled

Output





STEP 3: Development of the fuzzy rules

Based on CREAM basic diagram

Simple linguistic terms

Logical AND operation




CREAM basic diagram

Σimproved reliability

7 .654321

1 2 3 4 5 6 7 8 9 Σreduced reliability

Strategic Tactical Opportunistic Scrambled





Fuzzy model operations

Probability that operator performs

erroneous actionInput values

Fuzzification

Inference

Defuzzification




Scenarios

Five independent scenarios characterizing 5 different industrial contexts:

Scenario 2 represents a best case scenario

Scenario 4 represents a worst case scenario

Scenarios 4 and 5 have slight differences in the

values of input parameters




Results of test runs

1.91*10-1

2.02*10-1

6.33*10-2

9.81*10-4

1.0*10-2

Fuzzy Model results

1.0*10-1<p<1.0*100

1.0*10-1<p<1.0*100

1.0*10-2<p<0.5*100

0.5*10-5<p<1.0*10-2

1.0*10-3<p<1.0*10-1

Probability

interval

Scrambled5

Scrambled4 (Worst case)

Opportunistic3

Strategic2 (Best

case)

Tactical1

Control Mode

Scenario




Comments on the results

All FL model results in accordance with CREAM Best case scenario very low action failure

probability Worst case scenario very high action failure

probability Small differences in input have impact to output The results can be used directly in PSA methods

(event trees, fault trees, etc.)




Conclusions (1)

FL system to estimate the probability of human

erroneous action has been developed:

Based on CREAM methodology

9 input variables

1 output parameter




Conclusions (2)

Test runs for 5 different scenarios

Very satisfactory results

Main difference between FL model and CREAM:

probabilities estimation are exact numbers

The results can and will be used in other PSA

methods




Further goals

1) Model calibration with data from the Greek Petrochemical Industry

2) Addition of other CPCs or PSFs

3) Expansion to other fields of the chemical industry

4) Application in other fields of technology

(e.g aviation technology, maritime transports, etc…)




Acknowledgments

The Financial support of the EU Commission through project “PRISM” GTC1-2000-28030 to this research is kindly acknowledged

Documents

NCSR “DEMOKRITOS” Institute of Nuclear Technology and Radiation Protection NATIONAL TECHNICAL UNIVERSITY OF ATHENS School of Chemical Engineering Fuzzy